Summary of Automating Thought Of Search: a Journey Towards Soundness and Completeness, by Daniel Cao et al.
Automating Thought of Search: A Journey Towards Soundness and Completeness
by Daniel Cao, Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract proposes a novel approach to planning problems by automating the Thought of Search (ToS) framework, which has achieved 100% accuracy on various datasets. The authors leverage large language models’ (LLMs) code generation and refinement capabilities to guide them towards generating sound and complete search components. By providing feedback from generic and domain-specific unit tests, the authors demonstrate that LLMs can solve planning problems without human intervention, achieving impressive results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers plan better by using language models to make decisions. Currently, people are involved in this process, but now machines can do it alone. The researchers trained language models to create search plans and tested them on many problems. They found that the models could solve all the problems perfectly, just like humans! This is a big step forward for computers’ ability to plan and make decisions. |
